Generalized fluctuation test for deciphering phenotypic switching within cell populations

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Generalized fluctuation test for deciphering phenotypic switching within cell populations The inherent probabilistic nature of biochemical reactions coupled with low-copy number components results in significant random fluctuations (noise) in mRNA/protein levels inside individual cells. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. A long-term vision of our lab is to develop new mathematical and computational tools for studying stochastic dynamics of cellular biochemical processes, and use these tools to systematically understand how noise affects biological function and phenotype. As a consequence of noise in gene product levels, single cells within an isoclonal population can differ in their expression profile and reside in different pheno- typic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time makes it a particularly hard phenomenon to characterize. Unexpectedly, phenotypic heterogeneity within a population can play important functional roles in diverse biological processes, from driving genetically-identical cells to different cell fates to allowing microbes and cancer cells to hedge their bets against uncertain environmental changes. The Luria-Delbrück experiment, also called the “Fluctuation Test", introduced 75 years ago, demonstrated that genetic mu- tations arise randomly in the absence of selection – rather than in response to selection – and led to a Nobel Prize. The innovation of this project is to leverage this classical experiment in conjunction with mathematical modeling to char- acterize reversible and irreversible switching between cell states. The key advantage of the proposed method is that it is general enough to be applied to any proliferating cell type, and only involves making a single endpoint measurement. This is especially important for scenarios where a measurement involves killing the cell (for example, assaying whether a bacte- rial cell is in a drug-sensitive or drug-tolerant state or doing RNA-sequencing), and hence the state of the same cell cannot be measured at different time points. The project will develop mathematical tools for characterizing phenotypic switching between an arbitrary number of states using the fluctuation test, and such techniques will for the first time differentiate between an irreversible cell-state transition via genetic alterations vs. a reversible epigenetic transition. These tools will be first benchmarked with in-silico generated data and then applied on experimental datasets investigating diverse prob- lems, including characterizing drug-tolerant states in bacterial/fungal cells, understanding differences in viral susceptibility between single human cells within the same clonal population, and uncovering the transient dynamics of stem cell states that bias individual cells to different differentiation fates. Our preliminary work reveals plasticity in drug-tolerant states in bacterial, fungal, and cancer cells with different inheritance timescales. To understand the origins of cell states, the project will develop computational tools for inferring causal interaction networks from single-cell expression data. These tools will uncover how network topologies change across cell states and modeling the stochastic dynamics of underlying biochemical networks will mechanistically capture transitions between states. Overall, tools developed through this project will result in a fundamental understanding of how single-cell difference arises from stochastic epigenetic processes without any changes to DNA, and drive translational approaches to perturb cell states for therapeutic benefit.